Table 2.
Initial study of pycaret.
| Model | MAE | MSE | RMSE | R2 | RMSLE | MAPE |
|---|---|---|---|---|---|---|
| CatBoost | 1383.53 | 8,258,035.897 | 2842.3398 | 0.92 | 3.368 | 1.4268 |
| Light Gradient Boosting Machine |
1425.90 | 8,645,953.306 | 2917.0007 | 0.9167 | 3.1948 | 1.444 |
| Gradient Boosting | 1525.33 | 9,042,987.79 | 2973.3297 | 0.9129 | 3.4837 | 1.9477 |
| Random Forest | 1387.55 | 9,132,938.701 | 2988.8308 | 0.9122 | 1.4369 | 1.3502 |
| Extreme Gradient Boosting |
1528.08 | 9,132,115.59 | 2989.7954 | 0.912 | 3.5136 | 1.9225 |
| Extra Trees | 1371.164 | 9,370,078.345 | 3032.71 | 0.9091 | 1.4207 | 1.2388 |
| K Neighbors | 1995.01 | 14,418,032.69 | 3783.2075 | 0.8593 | 1.8795 | 3.2709 |
| AdaBoost | 2658.09 | 16,246,782.77 | 4011.6881 | 0.8427 | 4.5131 | 3.6487 |
| Decision Tree | 1823.12 | 18,487,851.69 | 4231.6982 | 0.8231 | 1.7356 | 1.2967 |
| Linear | 3320.22 | 21,609,347.74 | 4633.5979 | 0.7902 | 5.0483 | 6.1362 |
| Lasso | 3321.05 | 21,609,322.51 | 4633.5942 | 0.7902 | 5.0499 | 6.1158 |
| Bayesian Ridge | 3328.44 | 21,619,397.04 | 4634.7018 | 0.7901 | 5.0601 | 6.103 |
| Ridge Regression | 3330.88 | 21,622,794.72 | 4635.0831 | 0.79 | 5.0622 | 6.0973 |
| Lasso Least Angle | 3341.40 | 21,757,851.6 | 4649.9118 | 0.7888 | 5.0506 | 5.7934 |
| Random Sample Consensus |
3260.63 | 21,838,823.48 | 4657.6009 | 0.7878 | 5.0006 | 6.0694 |
| TheilSen Regressor | 3436.71 | 22,350,259.85 | 4713.5883 | 0.7825 | 5.1132 | 6.0852 |
| Huber Regressor | 3054.06 | 23,610,757.4 | 4837.1228 | 0.7706 | 4.615 | 4.3961 |
| Passive Aggressive | 3018.56 | 26,104,737.57 | 5088.1992 | 0.747 | 4.1218 | 3.0214 |
| Elastic Net | 4701.35 | 37,866,615.25 | 6143.6763 | 0.6359 | 5.2276 | 8.0117 |
| Orthogonal Matching Pursuit |
4881.56 | 38,453,549.95 | 6193.5956 | 0.6299 | 5.3537 | 5.5916 |
| Least Angle Regression |
5456.26 | 63,265,660.21 | 7134.0762 | 0.4037 | 5.4275 | 10.456 |
| Support Vector Machine |
6928.34 | 147,606,069.7 | 12,099.8444 | -0.3985 | 4.5004 | 1.4865 |